[1] ".GlobalEnv" "package:RSQLite" "package:tictoc" "package:car"
[5] "package:carData" "package:forcats" "package:stringr" "package:dplyr"
[9] "package:purrr" "package:readr" "package:tidyr" "package:tibble"
[13] "package:ggplot2" "package:tidyverse" "package:MASS" "package:knitr"
[17] "package:stats" "package:graphics" "package:grDevices" "package:utils"
[21] "package:datasets" "package:methods" "Autoloads" "package:base"
Todos los ejemplos han sido extraÃdos del libro R in action de Rober Kabacoff. Capitulo 19
library(ggplot2)
p<-ggplot(data=mtcars, aes(x=wt, y=mpg)) +
geom_point(pch=17, color="blue", size=2) +
geom_smooth(method="lm", color="red", linetype=2) +
labs(title="Automobile Data", x="Weight", y="Miles Per Gallon")
show(p)
# ?mtcars
mtcars$am <- factor(mtcars$am, levels=c(0,1),
labels=c("Automatic", "Manual"))
mtcars$vs <- factor(mtcars$vs, levels=c(0,1),
labels=c("V-Engine", "Straight Engine"))
mtcars$cyl <- factor(mtcars$cyl)
#Next, generate a plot using the following code:
library(ggplot2)
ggplot(data=mtcars, aes(x=hp, y=mpg,
shape=cyl, color=cyl)) +
geom_point(size=3) +
facet_grid(am~vs) +
labs(title="Automobile Data by Engine Type",
x="Horsepower", y="Miles Per Gallon")
data(singer, package="lattice")
ggplot(singer, aes(x=height)) + geom_histogram()
ggplot(singer, aes(x=voice.part, y=height)) + geom_boxplot()
# Reordenación de un factor, según una función aplicado a una variable
ggplot(singer, aes(x=reorder(voice.part,-height,mean), y=height)) + geom_boxplot()
data(Salaries, package="car")
library(ggplot2)
ggplot(Salaries, aes(x=rank, y=salary)) +
geom_boxplot(fill="cornflowerblue",
color="black", notch=TRUE)+
geom_point(position="jitter", color="blue", alpha=.5)+
geom_rug(side="l", color='black')
library(ggplot2)
data(singer, package="lattice")
ggplot(singer, aes(x=voice.part, y=height)) +
geom_violin(fill="lightblue") +
geom_boxplot(fill="lightgreen", width=.2)
data(Salaries, package="car")
library(ggplot2)
ggplot(data=Salaries, aes(x=salary, fill=rank)) +
geom_density(alpha=.3)
ggplot(Salaries, aes(x=yrs.since.phd, y=salary, color=rank,
shape=sex)) + geom_point()
ggplot(Salaries, aes(x=yrs.since.phd, y=salary, color=rank,shape=sex)) + geom_point()+scale_shape_manual(values=c(19,7))+scale_color_manual(values=c('black','red','green'))
ggplot(Salaries, aes(x=rank, fill=sex)) +
geom_bar(position="stack") + labs(title='position="stack"')
ggplot(Salaries, aes(x=rank, fill=sex)) +
geom_bar(position="dodge") + labs(title='position="dodge"')+scale_fill_manual(values=c('black','yellow'))+scale_x_discrete(labels=c('Profesor\nAsistente','Profesor\nAsociado','Profesor'))
ggplot(Salaries, aes(x=rank, fill=sex)) +
geom_bar(position="fill") + labs(title='position="fill"')
ggplot(Salaries, aes(x=rank, fill=sex))+ geom_bar()
ggplot(Salaries, aes(x=rank)) + geom_bar(fill="red")
ggplot(Salaries, aes(x=rank, fill="red")) + geom_bar()
data(singer, package="lattice")
library(ggplot2)
ggplot(data=singer, aes(x=height, fill=voice.part)) +
geom_density() +
facet_grid(voice.part~.)
data(Salaries, package="car")
library(ggplot2)
ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary)) +
geom_smooth() + geom_point()
ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary,
linetype=sex, shape=sex, color=sex)) +
geom_smooth(method=lm, formula=y~poly(x,2),
se=FALSE, size=1) +
geom_point(size=2)
data(Salaries,package="car")
library(ggplot2)
ggplot(data=Salaries, aes(x=rank, y=salary, fill=sex)) +
geom_boxplot() +
scale_x_discrete(breaks=c("AsstProf", "AssocProf", "Prof"),
labels=c("Assistant\nProfessor",
"Associate\nProfessor",
"Full\nProfessor")) + scale_y_continuous(breaks=c(50000, 100000, 150000, 200000),
labels=c("$50K", "$100K", "$150K", "$200K")) +
labs(title="Faculty Salary by Rank and Sex", x="", y="")
data(Salaries,package="car")
library(ggplot2)
ggplot(data=Salaries, aes(x=rank, y=salary, fill=sex)) +
geom_boxplot() +
scale_x_discrete(breaks=c("AsstProf", "AssocProf", "Prof"),
labels=c("Assistant\nProfessor",
"Associate\nProfessor",
"Full\nProfessor")) + scale_y_continuous(breaks=c(50000, 100000, 150000, 200000),
labels=c("$50K", "$100K", "$150K", "$200K")) +
labs(title="Faculty Salary by Rank and Gender",
x="", y="", fill="Gender") +
theme(legend.position=c(.1,.8))
ggplot(mtcars, aes(x=wt, y=mpg, size=disp)) +
geom_point(shape=21, color="black", fill="cornsilk") +
labs(x="Weight", y="Miles Per Gallon",
title="Bubble Chart", size="Engine\nDisplacement")
data(Salaries, package="car")
ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary, color=rank)) +
scale_color_manual(values=c("orange", "olivedrab", "navy")) + geom_point(size=2)
ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary, color=rank)) +
scale_color_brewer(palette="Set1") + geom_point(size=2)
library(RColorBrewer)
display.brewer.all()
Nota: cambiar package car–>carData ya que los conjuntos de datos se almacenan en una librerÃa distinta
data(Salaries, package="carData")
library(ggplot2)
mytheme <- theme(plot.title=element_text(face="bold.italic",
size="14", color="brown"),
axis.title=element_text(face="bold.italic",
size=10, color="brown"),
axis.text=element_text(face="bold", size=9,
color="darkblue"),
panel.background=element_rect(fill="white",
color="darkblue"),
panel.grid.major.y=element_line(color="grey",
linetype=1),
panel.grid.minor.y=element_line(color="grey",
linetype=2),
panel.grid.minor.x=element_blank(),
legend.position="top")
ggplot(Salaries, aes(x=rank, y=salary, fill=sex)) +
geom_boxplot() +
labs(title="Salary by Rank and Sex", x="Rank", y="Salary") +
mytheme
data(Salaries, package="carData")
library(ggplot2)
p1 <- ggplot(data=Salaries, aes(x=rank)) + geom_bar()
p2 <- ggplot(data=Salaries, aes(x=sex)) + geom_bar()
p3 <- ggplot(data=Salaries, aes(x=yrs.since.phd, y=salary)) + geom_point()
library(gridExtra)
grid.arrange(p1, p2, p3, ncol=3)
grid.arrange(p1, p2, p3, nrow=3)
grid.arrange(p1, p2, p3, nrow=2, ncol=2)
myplot <- ggplot(data=mtcars, aes(x=mpg)) + geom_histogram()
ggsave(file="mygraph.png", plot=myplot, width=5, height=4)
library(plotly)
data(Salaries, package="carData")
library(ggplot2)
mytheme <- theme(plot.title=element_text(face="bold.italic",
size="14", color="brown"),
axis.title=element_text(face="bold.italic",
size=10, color="brown"),
axis.text=element_text(face="bold", size=9,
color="darkblue"),
panel.background=element_rect(fill="white",
color="darkblue"),
panel.grid.major.y=element_line(color="grey",
linetype=1),
panel.grid.minor.y=element_line(color="grey",
linetype=2),
panel.grid.minor.x=element_blank(),
legend.position="top")
p<-ggplot(Salaries, aes(x=rank, y=salary, fill=sex)) +
geom_boxplot() +
labs(title="Salary by Rank and Sex", x="Rank", y="Salary") +
mytheme
ggplotly(p)
q<-ggplot(mtcars, aes(x=wt, y=mpg, size=disp)) +
geom_point(shape=21, color="black", fill="cornsilk") +
labs(x="Weight", y="Miles Per Gallon",
title="Bubble Chart", size="Engine\nDisplacement")
ggplotly(q)